signpost 0.1.0

Simple decorators and functions for type checking pandas DataFrames at runtime

This is a simple library for annotating and enforcing properties of pandas DataFrames at runtime. By showing which columns and types are expected before execution of a function begins, we can catch errors earlier with a message that makes sense and also document the inputs and outputs of functions more concisely.

Example Usage

Here is an example of standard usage to decorate a fairly strange function. Note that any valid pandas index value can be used, including numbers. We can combine Property’s together using And and Or if desired as well as qualify them using “all”, “any”, “just”, or “none”.

from signpost import Cols, Schema, Values, Superkey, And, df_args, df_return

@df_args(
    And(Cols("all", ["thing_1", 2]), Superkey(["thing_1"], over=[2])),
    other=Schema("just", {"thing_2": int, "thing_3": "string"})
)
@df_return(
    None,
    And(
        Cols("all", ["thing_1", "thing_2", "thing_3"]),
        Values("any", {"thing_1": [1, 2], "thing_3": ["foo", "bar"]}),
        Values("none", {"thing_1": [3]}),
    )
)
def do_a_thing(df: pd.DataFrame, other: pd.DataFrame) -> (int, pd.DataFrame):
    ...

However, there are times when the particular properties of a data frame depend on other inputs to a function. For example, a function may take a list of columns to subset by or a set of values to query with. This behavior is somewhat analogous to a function taking a List[T] and a parameter of type T – we are essentially making the data frame generic over a parameter specified by the caller. In these cases, we can use the Meta constructor, which is constructed with a string of Python code. The code is then evaluated with the environment of the function. For example, we can implement a checked “project” function (analogous to SELECT DISTINCT in SQL) as follows:

from signpost import df_args, df_return, Cols, Meta

@df_args(Cols("all", Meta("cols")))
@df_return(Cols("just", Meta("cols")))
def project(df: pd.DataFrame, cols: List[str]):
    return df.loc[:, cols].drop_duplicates()

Since the expressions passed to these meta properties can be arbitrary Python strings, we can express some fairly powerful logic using relatively little code. Note that since pandas DataFrames are dict-like, we can treat them as sequences of column names.

from signpost import df_args, df_return, Cols, Meta

@df_args(left=Cols("any", Meta("right")), right=Cols("any", Meta("left")))
@df_return(Cols("just", Meta("set(left) | set(other)"))
def merge(left, right):
    return pd.merge(left, right)

Extending signpost

There are a couple of ways to extend signpost. The first is using the Function property. It simply accepts a function that takes a pandas DataFrame and a context dictionary and returns a Optional[str].

from signpost import df_args, Function

@df_args(Function(lambda df, context: "bad" if df.empty else None))
def do_another_thing(df: pd.DataFrame):
    ...

It is also possible to create new Property’s simply by implementing the Property or ContextProperty interface found in signpost.properties.

TODO

There are a couple of improvements to be made, namely

1. Ergonomics. Assume bare types to be single-element lists.

2. Documentation.